3
The individuals All individuals are numerically described by a unique strategy vector (easy think of it as genes): All individuals’ states are described in the attribute vector: Strategy vector (length n) …. n 1.6 kg 590 days 34g fat female 303 eggs Attribute vector

4
Super-individuals There is, depending on model complexity, an upper practical limit to how many individuals that can be simulated In models where the number or biomass of individuals are important and very high, a way around this problem is to treat each individual as a super-individual A super-individual simply has a number added to its attribute vector telling how many (identical) individuals it represents 500 ind 590 days 34g fat female 303 eggs Attribute vector

5
The genetic algorithm (GA) A GA is an algorithm that mimics evolution by natural selection. So - what is required to make evolution possible? 1.A population of individuals 2.Genetic variability among individuals 3.A genotype – phenotype relationship 4.Individual variation in phenotypic success (fitness) 5.Inheritability of genotypes from one generation to the next 6.Introduction of new genetic variance (at least in the long run) How is this implemented in a GA?

9
Implementing a GA - IV +=or Strategy vectors 1.A population of individuals 2.Genetic variability among individuals 3.A genotype – phenotype relationship 4.Individual variation in fitness 5.Inheritability of genotypes from one generation to the next 6.Introduction of new genetic variance

10
About fitness (or: who gets to reproduce?) There are two distinctly different ways to incorporate fitness in an ING-model –By using a fitness measure (applied fitness) sort all individuals in the population according to the fitness measure and only let the fit ones reproduce. A fitness measure is imposed on the population. Replace the old generation with the new one. No chance of extinction. No population dynamics. –By simulating the individuals’ entire life-span including mortality, gonad development, foraging, metabolic expenditure, etc… (emergent fitness) individuals will reproduce off-spring according to how well they adapted they are to the environment. Fitness becomes an emergent property of the model. The off-spring is added to the population as juveniles and do not replace existing individuals. Emergent population dynamics. Population may go extinct.

17
Artificial Neural Network – Transformation After obtaining the value HiddenNode j the value is transformed non- linearly. Most often a sigmoid function is used. A bias is also often included. HiddenNode jT HiddenNode j

20
ING-models: Pros and cons Cons –No guarantee that the optimal solution is found –Need to run replicate simulations –Can be difficult to “decode” the adapted neural network ANN = black box? Pros –Can incorporate very high levels of complexity: Stochasticity, Intra- and Inter-specific competition –Can be used to study emergent patterns on different levels simultaneously: Population dynamics, state-dependent behaviour –Can avoid using a measure of fitness by making fitness an emergent property of the model.

21
Example: A model of a planktivours fish Strand, E., Huse,G., Giske, J. (2002) Time resolution –Simulates 1 day every month (and scales it to the entire month) –Each day is divided into 5 minutes time-steps –Run for several hundred generations Behaviour and life-history strategy –Depth position –Energy allocation –Spawning strategy Emergent fitness Main focus –Differences in juvenile and adult behaviour –Effects from stochastic juvenile survival on life-history and behaviour